Mathematical models are often built to capture complex interrelationships between input parameters and output parameters. Various techniques may be used in such models to establish correlations between input parameters and output parameters. Once the models are established, the models predict the output parameters based on the input parameters. The accuracy of these models often depends on the environment within which the models operate.
One tool that has been developed for mathematical modeling is U.S. Pat. No. 6,751,536 to Kipersztok et al. (the '536 patent). The '536 patent describes a system and method for performing diagnostic modeling to determine a maintenance action for an airplane. The system receives input relating to symptoms indicative of a failed component in an aircraft and creates a prioritized list of components that could be causing the symptoms of failure. The system may employ a Bayesian network to identify the probability that each component caused the failure, and may use cost calculations to determine whether to replace a failed component.
Although the tool of the '536 patent offers a recommendation of whether to replace a component based on a failure, the '536 patent cannot predict whether a component will fail in the future. The failure of a single component may lead to the failure of other components and to increased downtime as the machine must be suddenly taken out of service to repair the failed components. In the field of medical diagnostics, discovering that a patient already has a serious health problem may not offer a significant chance of survival. Many chronic conditions, such as heart disease, diabetes, and certain forms of cancer, can sometimes be avoided if certain lifestyle modifications can be made sufficiently prior to disease onset. Just as in the machine case, certain progressive diseases can arise from the onset of another disease. For instance, persons who contract Type II diabetes increase their risk of cardiovascular disease, which in turn increases the risk of a stroke. Machine repair, maintenance staff, and physicians would prefer a system and method that could predict when a component will fail or when a health condition would become serious, allowing the opportunity to prevent the failure and avoid the complications that arise after a component has already failed.
The present disclosure is directed to overcoming one or more of the problems set forth above.